1994 — 1998 |
Harris, Gordon J |
R29Activity Code Description: Undocumented code - click on the grant title for more information. |
Neuroimaging in Persons At Risk For Huntington's Disease
This project will utilize neuroimaging techniques to identify brain abnormalities in persons at risk for Huntington's disease (HD). The specific aims include: Documentation of the earliest neuroimaging evidence of HD cross-sectionally in persons who have been identified through DNA testing as having the linked genetic marker, and therefore very likely having the gene; evaluation of structural changes in the brain (particularly the putamen) using magnetic resonance imaging (MRI); evaluation of regional cerebral blood flow (rCBF) changes in the cerebral cortex and basal ganglia using single photon emission computed tomography (SPECT); correlation of neuroimaging results with assessment of cognitive, emotional, and motor impairment. Subjects will be followed longitudinally to assess progression or emergence of neuroimaging changes relative to progression or emergence of clinical symptoms. There have been no prior quantitative SPECT or volumetric MRI studies in persons at risk for Huntington's disease, nor has any prior study tracked the emergence and progression of neuroimaging and clinical abnormalities in subjects with informative genetic tests for HD. Subjects will be participants in the Johns Hopkins program of predictive testing for Huntington's disease. At-risk subjects who have informative genetic testing results (probability > 95% for HD gene) will be asked to participate in the neuroimaging study. Gene-marker negative (probability < 5%) subjects will be used as controls. Groups will be matched for age, sex, race, education and socio-economic status. Marker-positive subjects will receive one SPECT scan and one MRI scan each year for five years. Marker-negative controls will receive 2 scan pairs in five years. MRI scans will be quantitatively measured for caudate and putamen volumes, bicaudate ratio, whole brain volume and cerebro-spinal fluid volume. SPECT scans will be rated for regional cortical and subcortical rCBF values. Neurologic and neuropsychological tests will be given at least annually, and results will be correlated with neuroimaging changes.
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0.928 |
2006 — 2009 |
Harris, Gordon J |
P50Activity Code Description: To support any part of the full range of research and development from very basic to clinical; may involve ancillary supportive activities such as protracted patient care necessary to the primary research or R&D effort. The spectrum of activities comprises a multidisciplinary attack on a specific disease entity or biomedical problem area. These grants differ from program project grants in that they are usually developed in response to an announcement of the programmatic needs of an Institute or Division and subsequently receive continuous attention from its staff. Centers may also serve as regional or national resources for special research purposes. |
Neuroimaging Core @ Massachusetts General Hospital
The mission of the SPOTRIAS Neuroimaging Core is to develop, install, and maintain the technology infrastructure and provide services to support the neuroimaging components of the three SPOTRIAS research projects. These Core functions will include the following components: image communication to enable image transfer from the picture archive and communication systems (PACS) at the participating SPOTRIAS hospitals (Massachusetts General Hospital (MGH) and Brigham and Women's Hospitals (BWH), Boston, MA) to the Neuroimaging Core located at MGH, anonymization/blinding of the images, providing workstations and support for radiological image review, performing quantitative measurements of lesion size on the various images, radiological data capture and management, and translation of imaging results to the database/statistics core for statistical analysis in relation to the clinical initial and outcomes data. All three SPOTRIAS research projects include stroke neuroimaging evaluation and analysis, with patients who may participate in more than one of these three research projects. The Neuroimaging Core will serve to unify these functions across the SPOTRIAS research projects. Thus, this Core will provide standardized, consistent, and efficient radiological measurements of lesion size on the non-contrast CT scans, the CTA source images and the CBV, CBF, and MTT maps. Resulting data will be shared among overlapping projects. The Core will provide the imaging data that will serve as independent, verifiable measurement of change in lesion size for patients in the normobaric oxygen therapy treatment project, and will additionally serve as a centralized computerized resource to enable efficient neuroimaging storage, review, and measurements. The primary goal of the Neuroimaging Core is to develop a centralized facility for radiological review of scans, and also for making reliable, quantitative volumetric measurements of strokerelated lesions from serial CT images (including non-contrast CT, CTA source and maximum intensity projection images, CT perfusion, etc.). Results of analyses will be imported into the SPOTRIAS database, which will also contain clinical information from all three projects. The Core will manage implementation of infrastructure and software for creating a centralized service to provide quantitative and radiological imaging metrics for all three projects, with images originating from both participating SPOTRIAS hospitals (MGH and BWH).
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0.907 |
2015 — 2019 |
Harris, Gordon J |
U24Activity Code Description: To support research projects contributing to improvement of the capability of resources to serve biomedical research. |
Extensible Open-Source Zero-Footprint Web Viewer For Oncologic Imaging Research @ Massachusetts General Hospital
? DESCRIPTION (provided by principal investigator): Managing the increasingly complex workflow and imaging analyses for oncology clinical trials to provide timely, protocol-compliant assessments requires sophisticated informatics tools. To address these issues, over the past 10 years the Tumor Imaging Metrics Core (TIMC), a CCSG Shared-Resource of the Dana-Farber/Harvard Cancer Center (DF/HCC), has developed a software informatics system for managing the workflow and image measurements for oncology clinical trials. This system currently is in use across the 5 DF/HCC Harvard hospitals to manage over 600 active clinical trials, with 800 users, and has been licensed and implemented at several other Cancer Centers, including Yale, Utah/Huntsman Cancer Institute, and UW/Seattle Cancer Care Alliance. The workflow management informatics system includes a web-application/database for protocol registration, order entry, work list management, reporting, and billing/administrative functions. However, the integrated, open-source image analysis platform is workstation-based and needs to be installed and updated on each computer where imaging measurements will be performed. Thus, the open- source imaging system is a limiting factor in deployment, requiring added IT desktop support and maintenance at each location where imaging assessments will be performed. The widespread adoption of this system across NCI Cancer Centers will be aided and facilitated by adapting the image analysis functionality into a web-based open-source platform. The current proposal is to 1) create a vendor-neutral, open-source, extensible, zero-footprint web-viewer and supporting server for display and analysis of DICOM images, and 2) create a plug-in architecture to allow us to replace our current open-source image workstation with this zero-footprint web-viewer for our TIMC clinical trials management system. In order to make the system broadly available to the oncology research community, in addition to developing an interface to our TIMC web-application, we will also implement an AIM (Annotation and Image Markup) interface so that caBIG investigators and other AIM framework research projects can integrate easily with our web-viewer. The viewer will meet all of the basic requirements for radiology tumor measurements specific to the needs of oncology clinical trials, yet also be flexible enough to be configured for user preferences and extended via plug-ins to support varied research workflows as a shared research resource. To achieve these design goals, the viewer and all of its functionality will be delivered to client machines exclusively through the web browser with nothing to install on client computers or mobile devices, which greatly simplifies and reduces the cost and support requirements of software deployments, and increases accessibility. The proposed viewer will enable researchers, imaging software developers, clinicians, and patients to access oncology clinical trials images in a freely availabl and openly extensible environment. This will facilitate remote image viewing and collaborative image consultations among a wide-range of imaging professionals. The web-DICOM viewer will be fully integrated with the TIMC clinical trials informatics management system so that both systems can be deployed across NCI Cancer Centers in a completely web-based implementation without desktop IT support. An AIM interface will also be provided so that the web-viewer can be integrated broadly across the caBIG/AIM cancer research community.
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0.907 |
2019 — 2021 |
Harris, Gordon J Kikinis, Ron [⬀] |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Lymph Node Quantification System For Multisite Clinical Trials @ Brigham and Women's Hospital
Project Summary / Abstract In patients with lymphomas and other cancers, quantitative evaluation of the extent of tumor burden is im- portant for staging, restaging, and assessment of therapeutic response or relapse; yet measurement of overall tumor burden is challenging with current tools, particularly when lymph nodes are confluent or difficult to fully differentiate from surrounding structures. Precision medicine and novel therapeutics are emphasizing the need to introduce a risk-adapted approach to tailor appropriate treatment strategies for cancer patients. The ability to quantitatively assess cancer phenotypes with functional and anatomical imaging that could efficiently and ac- curately map patients to gene expression profiling, clinical information, matching cohorts, and novel treatment regimens could potentially result in more optimal management of patients with cancer. This Academic-Industry Partnership aims to translate recently developed technologies for semi- automated image segmentation and quantification of lymph nodes into robust tools and integrate them into an existing cloud-based system for management of multicenter oncology clinical trials. The ability to semi- automatically segment lymph node pathology with computed tomography (CT), as well as quantify nodal me- tabolism with positron emission tomography (PET) will enable comprehensive tracking of morphological and functional changes related to disease progression and treatment response. Since 2004, the Dana-Farber/Harvard Cancer Center's (DF/HCC) Tumor Imaging Metrics Core (TIMC) has developed the Precision Imaging Metrics, LLC (PIM) platform to manage clinical trial image assessment workflows. Currently, there are nearly 50,000 consistently measured lymph node measurements in the TIMC database. The PIM system is used to make over 20,000 time point imaging assessments per year at eight NCI- designated Cancer Centers and aims to grow quickly by transitioning to a fully cloud-hosted system. Given sufficient training data, state-of-the-art machine learning and artificial intelligence (AI) technolo- gies can meet or even exceed human performance on specific imaging analysis tasks. Recent studies have indicated that AI-based lymph node segmentation from CT scans is nearing human performance levels, and we will extend and translate this work into a commercial tool. Specifically, our aim is to translate recent ad- vancements in AI-based segmentation into deployable services, and integrate these services into the clinical trial workflow. The proposed system will be designed to incorporate expert feedback provided by image ana- lysts and radiologists back into the ground truth dataset, allowing for continuous improvement in accuracy and clinical acceptance. We will extend our semi-automatic CT segmentation technologies to quantify lymph node metabolism in PET/CT, using lymphoma as the model disease. Integration of these technologies with PIM will provide an ongoing source of consistently measured quantitative data across a network of cancer centers.
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0.91 |